Analysis of the association between racial inequities and edentulism in Brazil: a systematic review and meta-analysis
Bibliographic record
Abstract
This study aimed to evaluate whether individuals who self-identify as black and/or mixed-race have a higher prevalence of tooth loss compared to white individuals in Brazil, using a systematic review and meta-analysis. Searches were conducted in the PubMed, Scopus, Web of Science, Virtual Health Library, Embase, and gray literature databases. Two independent reviewers performed the searches and article selection processes. The Newcastle-Ottawa Scale was used for observational cohort studies, and its modified version was used for cross-sectional studies. The I2 statistic assessed the heterogeneity of studies included in the meta-analyses. Of the 25 articles eligible for qualitative evaluation, 17 were included in the quantitative assessment. Sample sizes ranged from 101 to 18,718 individuals aged 11 to 74 years. Most studies compared white individuals to non-white individuals (black, mixed-race, Asian, and Indigenous people). In the comparison between white and non-white individuals, no differences were found concerning edentulism (OR = 0.86; 95%CI: 0.71; 1.06), absence of functional dentition (OR = 0.82; 95%CI: 0.33; 2.03), or mean number of missing teeth (MD = -0.21; 95%CI: -2.92; 2.49), but it was associated with tooth loss (OR = 1.40; 95%CI: 1.26; 1.55). When comparing black/mixed-race people to white individuals, tooth loss was higher among those who self-identified as black/mixed-race (OR = 1.41; 95%CI: 1.27; 1.57). This difference was also observed when comparing black/mixed-race individuals to other races/skin color (OR = 1.24; 95%CI: 1.15; 1.33). Overall, studies conducted in Brazil found that tooth loss was more prevalent among self-declared black and/or mixed-race individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".